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Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome
Open accessAbstract
The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.
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“The BraTS challenge35–38, seeking methodological advancements in the domain of neuro-oncology, has been providing the community with (i) the largest publicly available and manually-curated mpMRI dataset of diffuse glioma patients (an example of which is illustrated in Fig. 1b), and (ii) a harmonized preprocessing pipeline51,70,71 to handle differences in inter-site acquisition protocols.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [51]
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Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Verhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2009.12.020 · Cited by 7,575
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067
Cortazar, P., Zhang, L., Untch, M., Mehta, K. M., Costantino, J. P., Wolmark, N., Bonnefoi, H. R., Cameron, D., Gianni, L., Valagussa, P., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(13)62422-8 · Cited by 4,777
SUSAN—a new approach to low level image processing
Smith, S. M., & Brady, J. M. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007963824710 · Cited by 3,333
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049
Self-citationPaik, S., Tang, G., Shak, S., Kim, C., Baker, J. B., Kim, W., Cronin, M. T., Baehner, F. L., Watson, D., Bryant, J., et al. (2006) · Journal of Clinical Oncology · doi:10.1200/jco.2005.04.7985 · Cited by 2,597
Texture analysis using gray level run lengths
Galloway, M. M. (1975) · Computer Graphics and Image Processing · doi:10.1016/s0146-664x(75)80008-6 · Cited by 2,168
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Items 101–200 of 202
Dohm, A. E., Nickles, T. M., Miller, C. E., Bowers, H. J., Miga, M. I., Attia, A. A., Chan, M. D., & Weis, J. A. (2021) · Medical Physics · doi:10.1002/mp.14999 · Cited by 12
Wang, H. (2025) · PLoS ONE · doi:10.1371/journal.pone.0326361 · Cited by 11
Zhao, Y., Tan, F., Zhao, J., Zhou, S., Luo, Y., & Chen, G. (2025) · MedComm · doi:10.1002/mco2.70202 · Cited by 13
Rathore, S., Akbari, H., Bakas, S., Pisapia, J. M., Shukla, G., Rudie, J. D., Da, X., Davuluri, R. V., Dahmane, N., O’Rourke, D. M., et al. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00081 · Cited by 11
Self-citationRole of MRI radiomics for the prediction of MYCN amplification in neuroblastomas
Ghosh, A., Yekeler, E., Teixeira, S. R., Dalal, D. J., & States, L. J. (2023) · European Radiology · doi:10.1007/s00330-023-09628-7 · Cited by 12
Predictive analytics and early intervention in healthcare social work: a scoping review
Kumar, D., & Suthar, N. (2024) · Social Work in Health Care · doi:10.1080/00981389.2024.2316700 · Cited by 15
Chitalia, R., Miliotis, M., Jahani, N., Tastsoglou, S., McDonald, E. S., Belenky, V., Cohen, E. A., Newitt, D. C., Veer, L. J. V., Esserman, L. J., et al. (2023) · Communications Medicine · doi:10.1038/s43856-023-00273-1 · Cited by 10
Self-citationSurvival analysis using deep learning with medical imaging
Morrison, S., Gatsonis, C. A., Eloyan, A., & Steingrimsson, J. A. (2023) · The International Journal of Biostatistics · doi:10.1515/ijb-2022-0113 · Cited by 9
Gao, M., Cheng, J., Qiu, A., Zhao, D., Wang, J., & Liu, J. (2024) · Clinical Radiology · doi:10.1016/j.crad.2024.08.005 · Cited by 9
Zhuang, Z., Lin, J.-X., Wan, Z., Weng, J.-R., Yuan, Z., Xie, Y.-M., Liu, Z., Xie, P., Mao, S., Wang, Z., et al. (2024) · BMC Medicine · doi:10.1186/s12916-024-03573-y · Cited by 10
Chakrabarty, S., Abidi, S. A., Mousa, M. S., Mokkarala, M., Hren, I., Yadav, D., Kelsey, M., LaMontagne, P., Wood, J., Adams, M., et al. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00177 · Cited by 11
Self-citationOptimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments
Thakur, S. P., Pati, S., Panchumarthy, R., Karkada, D., Wu, J., Kurtaev, D., Sako, C., Shah, P., & Bakas, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08999-2_12 · Cited by 9
Self-citationBakas, S., Akbari, H., Shukla, G., Rozycki, M., Davatzikos, C. A., & Rathore, S. (2018) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2293661 · Cited by 9
Self-citationLi, H., Conte, G. M., Hu, Q., Anwar, S. M., Kofler, F., Ezhov, I., Leemput, K. V., Piraud, M., Diaz, M., Cole, B., et al. (2024) · PubMed · Cited by 9
Self-citationRepurposing the Public BraTS Dataset for Postoperative Brain Tumour Treatment Response Monitoring
Sørensen, P. J., Ladefoged, C. N., Larsen, V. A., Andersen, F. L., Nielsen, M. B., Poulsen, H. S., Carlsen, J. F., & Hansen, A. E. (2024) · Tomography · doi:10.3390/tomography10090105 · Cited by 8
Namdar, K., Wagner, M. W., Ertl-Wagner, B. B., & Khalvati, F. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01855-2 · Cited by 8
Completed Feature Disentanglement Learning for Multimodal MRIs Analysis
Liu, T., Liu, H., Shang, F., Yu, L., Han, T., & Wan, L. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3539712 · Cited by 9
A New Logic for Pediatric Brain Tumor Segmentation
Bengtsson, M., Keleş, E., Durak, G., Anwar, S. M., Velichko, Y., Linguraru, M. G., Waanders, A. J., & Bağcı, U. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980809 · Cited by 7
Duman, A., Karakuş, O., Sun, X., Thomas, S., Powell, J., & Spezi, E. (2023) · Cancers · doi:10.3390/cancers15235620 · Cited by 7
Boelders, S. M., Baene, W. D., Postma, E. O., Gehring, K., & Ong, L. L. (2024) · Neuroinformatics · doi:10.1007/s12021-024-09671-9 · Cited by 7
Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression
Pati, S., Sharma, V., Aslam, H., Thakur, S. P., Akbari, H., Mang, A., Subramanian, S., Biros, G., Davatzikos, C. A., & Bakas, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72084-1_15 · Cited by 7
Self-citationThakran, S., Cohen, E. A., Jahani, N., Weinstein, S. P., Pantalone, L., Hylton, N. M., Newitt, D. C., DeMichele, A. M., Davatzikos, C. A., & Despina Kontos, B. (2022) · Translational Oncology · doi:10.1016/j.tranon.2022.101411 · Cited by 7
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Grenko, C. M., Viaene, A. N., Nasrallah, M. P., Feldman, M. D., Akbari, H., & Bakas, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46640-4_5 · Cited by 7
Self-citationRadiogenomic modeling predicts survival-associated prognostic groups in glioblastoma
Nuechterlein, N., Li, B., Feroze, A. H., Holland, E. C., Shapiro, L. G., Haynor, D. R., Fink, J. R., & Cimino, P. J. (2021) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdab004 · Cited by 7
Venet, L., Pati, S., Yushkevich, P. A., & Bakas, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.11929 · Cited by 7
Self-citationChen, C., Deng, M., Zhong, Y., Cai, J., Chan, K. K. W., Dou, Q., Chong, K. K. L., Heng, P., & Chu, C. W. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3545138 · Cited by 6
Sex-Specific Differences in Patients withIDH1–Wild-Type Grade 4 Glioma in the ReSPOND Consortium
Gongala, S., García, J., Korakavi, N., Patil, N. T., Akbari, H., Sloan, A. E., Barnholtz‐Sloan, J. S., Sun, J., Griffith, B. D., Poisson, L., et al. (2024) · American Journal of Neuroradiology · doi:10.3174/ajnr.a8319 · Cited by 6
Self-citationZhang, S., Richter, J., Veale, J., Phan, V. M. H., Candy, N. G., Poonnoose, S. I., Agzarian, M., & To, M. (2025) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2025.111118 · Cited by 6
Bakas, S., Shukla, G., Akbari, H., Erus, G., Sotiras, A., Rathore, S., Sako, C., Ha, S. M., Rozycki, M., Singh, A., et al. (2020) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.2566505 · Cited by 6
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Bounias, D., Singh, A., Bakas, S., Pati, S., Rathore, S., Akbari, H., Bilello, M., Greenberger, B. A., Lombardo, J. F., Chitalia, R., et al. (2021) · Applied Sciences · doi:10.3390/app11167488 · Cited by 6
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Boché, A., Landras, A., Morel, M., Kellouche, S., Carreiras, F., & Lambert, A. (2024) · Journal of Cellular Physiology · doi:10.1002/jcp.31491 · Cited by 8
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Nunnari, F., & Sonntag, D. (2021) · ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS) · doi:10.1145/3459926.3464753 · Cited by 6
Acciavatti, R. J., Cohen, E. A., Maghsoudi, O. H., Gastounioti, A., Pantalone, L., Hsieh, M.-K., Conant, E. F., Scott, C. G., Winham, S. J., Kerlikowske, K. M., et al. (2021) · Cancers · doi:10.3390/cancers13215497 · Cited by 6
Self-citationFamiliar, A., Kazerooni, A. F., Anderson, H., Lubneuski, A., Viswanathan, K., Breslow, R., Khalili, N., Bagheri, S., Haldar, D., Kim, M. C., et al. (2023) · PubMed · doi:10.48550/arxiv.2310.01413 · Cited by 9
Bajunaid, R., Niu, C., Hambly, C., Liu, Z., Yamada, Y., Alemán-Mateo, H., Anderson, L. J., Arab, L., Baddou, I., Bandini, L. G., et al. (2025) · Nature Food · doi:10.1038/s43016-025-01175-2 · Cited by 7
AI-powered segmentation and prognosis with missing MRI in pediatric brain tumors
Chrysochoou, D., Gandhi, D. B., Adib, S., Familiar, A., Vunnava, B., Varshochi, S., Khalili, N., Khalili, N., Ware, J. B., Tu, W., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-025-01269-x · Cited by 5
Self-citationFrom imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis
Wang, F., Chen, J., Zheng, L., & Huang, X. (2026) · Journal of bone oncology · doi:10.1016/j.jbo.2026.100753 · Cited by 6
Current and Future Applications of PET Radiomics in Radiation Oncology
Yong Fan, A., Feigenberg, S. J., & Simone, C. B. (2025) · PET Clinics · doi:10.1016/j.cpet.2025.01.002 · Cited by 5
Consistency and grade prediction of intracranial meningiomas based on fractal geometry analysis
Markia, B., Mezei, T., Báskay, J., Pollner, P., Mátyás, A., Simon, Á., Várallyay, P., Banczerowski, P., & Erőss, L. (2025) · Neurosurgical Review · doi:10.1007/s10143-025-03737-1 · Cited by 5
Rathore, S., Niazi, T. M., Iftikhar, M. A., Singh, A., Rathore, B., Bilello, M., & Chaddad, A. (2020) · Applied Sciences · doi:10.3390/app10061903 · Cited by 5
Self-citationMulti Modal Fusion for Radiogenomics Classification of Brain Tumor
Mun, T. S. H., Doran, S., Huang, P. H., Messiou, C., & Blackledge, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08999-2_29 · Cited by 5
Lei, M., Varghese, B. A., Hwang, D., Cen, S., Lei, X., Azadikhah, A., Desai, B., Oberai, A. A., & Duddalwar, V. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12761 · Cited by 5
Hossain, T., Qureshi, Z., Jayakumar, N., Muttikkal, T. J. E., Patel, S. H., Schiff, D., Zhang, M., & Kundu, B. K. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230599 · Cited by 4
Multivariate testing and effect size measures for batch effect evaluation in radiomic features
Horng, H., Scott, C. G., Winham, S. J., Jensen, M., Pantalone, L., Mankowski, W. C., Kerlikowske, K. M., Vachon, C. M., Despina Kontos, B., & Shinohara, R. T. (2024) · Scientific Reports · doi:10.1038/s41598-024-64208-z · Cited by 4
Self-citationMachine learning in neuroimaging of epilepsy: a narrative review
Perillo, T., Perillo, S., & Pinto, A. (2024) · Journal of Medical Imaging and Interventional Radiology · doi:10.1007/s44326-024-00041-y · Cited by 9
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Adeola, H. A., Holmes, H., & Temilola, D. O. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.94192 · Cited by 4
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Ligneris, M. D., Bonnet, A., Chatelain, Y., Glatard, T., Sdika, M., Vila, G., Wargnier-Dauchelle, V., Camarasu-Pop, S., & Frindel, C. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230482 · Cited by 4
Cepeda, S., García-García, S., Arrese, I., Herrero, F., Escudero, T., Zamora, T., & Sarabia, R. (2023) · PubMed Central · doi:10.48550/arxiv.2305.00005 · Cited by 4
McCoy, J. C. S., Spicer, J. I., Rundle, S. D., & Tills, O. (2023) · Journal of Experimental Biology · doi:10.1242/jeb.245612 · Cited by 4
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Kazerooni, A. F., & Davatzikos, C. A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72084-1_3 · Cited by 4
Self-citationKundu, B. K., Qureshi, Z., Chavan, R., & Rahman, A. (2025) · Advances in cancer research · doi:10.1016/bs.acr.2025.07.001 · Cited by 3
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Druzhinina, P., Kondrateva, E., Bozhenko, A., Yarkin, V. E., Sharaev, M., & Kurmukov, A. G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08999-2_15 · Cited by 3
Zeng, Q., Tian, Z., Dong, F., Shi, F., Xu, P., Zhang, J., Ling, C., & Guo, Z. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1246730 · Cited by 3
Gandhi, D. B., Khalili, N., Familiar, A., Gottipati, A., Khalili, N., Tu, W., Haldar, S., Anderson, H., Viswanathan, K., Storm, P. B., et al. (2024) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdae190 · Cited by 3
Mei, N., Lu, Y., Yang, S., Jiang, S., Ruan, Z., Wang, D., Liu, X., Ying, Y., Li, X., & Yin, B. (2023) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000001454 · Cited by 3
Adewole, M., Rudie, J. D., Gbdamosi, A., Toyobo, O. O., Raymond, C., Zhang, D., Omidiji, O. A. T., Akinola, R., Suwaid, M. A., Emegoakor, A., et al. (2023) · PubMed · Cited by 3
Self-citationChakrabarty, S., Abidi, S. A., Mousa, M. S., Mokkarala, M., Kelsey, M., LaMontagne, P., Sotiras, A., & Marcus, D. S. (2023) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.2647656 · Cited by 2
Self-citationXu, L., Zhao, W., Guo, R., & Huang, X. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01788-w · Cited by 2
Matsumoto, Y., Singh, O., García, J., Abdullaev, Z., Freeburg, N. F., Yu, F., Akbari, H., Baik, K. H., Guo, J., Shih, N. N. C., et al. (2025) · Neuro-Oncology · doi:10.1093/neuonc/noaf175 · Cited by 2
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Perillo, T., de Giorgi, M., Papace, U. M., Serino, A., Cuocolo, R., & Manto, A. (2023) · Exploration of Targeted Anti-tumor Therapy · doi:10.37349/etat.2023.00151 · Cited by 3
Fatania, K., Frood, R. T., Mistry, H. B., Short, S. C., O’Connor, J. P. B., Scarsbrook, A. F., & Currie, S. (2024) · Cancers · doi:10.3390/cancers16071301 · Cited by 2
Ghosh, A., Li, H., Towbin, A. J., Turpin, B. K., & Trout, A. T. (2025) · Pediatric Radiology · doi:10.1007/s00247-025-06205-6 · Cited by 2
Self-citationBeig, N. G., Hill, V. B., Verma, R., Varadan, V., Madabhushi, A., Tiwari, P., & Prasanna, P. (2019) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2512258 · Cited by 2
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Tan, S. H., Guan, C. A., Bujang, M. A., Lai, W. H., Voon, P. J., & Sim, E. U. (2024) · Scientific Reports · doi:10.1038/s41598-024-52421-9 · Cited by 2
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Awujoola, O. J., Awujoola, A. E., Adelegan, O. R., Enem, A. T., & Abioye, O. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33498-6.00025-x · Cited by 1
Feng, Y., Wang, W., Zhang, X., Jing, Y., Yi, J., Xia, M., Cai, W., & Zhang, X. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107855 · Cited by 1
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Jung, G., Liang, Z., Jain, P., Deutsch, H. M., Li, F., Rathi, K. S., Sun, Z., Chen, X., Straka, J., Kazerooni, A. F., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01121-2 · Cited by 1
Yang, Q., Huang, B., Zhou, Q., Wang, Z., Chen, K., Xiu, T., Yao, C., & Wu, S. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3633279 · Cited by 1
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Yang, T., Wang, X., Liu, H., Gong, G., Wu, L., Wang, Y., & Wang, L. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103922 · Cited by 1
Mezei, T., Báskay, J., Markia, B., Várallyay, P., Banczerowski, P., & Pollner, P. (2026) · Neurosurgical Review · doi:10.1007/s10143-025-04078-9 · Cited by 1
Singh, A., & Bilello, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-40124-5_1 · Cited by 1
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Horng, H., Singh, A., Yousefi, B., Cohen, E. A., Haghighi, B., Katz, S. I., Noël, P. B., Shinohara, R. T., & Despina Kontos, B. (2022) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2610831 · Cited by 1
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Sherkhane, U. B., Jha, A. K., Mithun, S., Jaiswar, V., Traverso, A., Wee, L., Rangarajan, V., & Dekker, A. L. A. J. (2023) · F1000Research · doi:10.12688/f1000research.129826.1 · Cited by 1
Chen, A. W., Patel, O., Cohen, E. A., Roshkovan, L., & Despina Kontos, B. (2024) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3006944 · Cited by 1
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O’Rourke, D. M., Davatzikos, C. A., Rathore, S., Akbari, H., Bakas, S., Pisapia, J. M., & Da, X. (2019) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2512837
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Arthy, M., Theresa, M. M., Yasotha, B., Kavitha, S., & Samiappan, D. (2025) · De Gruyter eBooks · doi:10.1515/9783112205198-002
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Bornes, R., Montero, J., Correia, A., Marques, T., & Rosa, N. (2024) · Figshare · doi:10.6084/m9.figshare.26579243
Altaf, I., Jan, A., Ahmad, S. N., Mir, A. W., Thakur, N., & Mir, M. A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-23908-3.00001-0
Yang, T., Chen, N., Yao, L., & Wang, L. (2026) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-026-2910-4
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Metadata
- Authors
- Davatzikos, C., Rathore, S., Bakas, S., Pati, S., Bergman, M., Kalarot, R., Sridharan, P., Gastounioti, A., Jahani, N., Cohen, E., Akbari, H., Tunc, B., Doshi, J., Parker, D., Hsieh, M., Sotiras, A., Li, H., Ou, Y., Doot, R. K., Bilello, M., Fan, Y., Shinohara, R. T., Yushkevich, P., Verma, R., & Kontos, D.
- Affiliations
- University of Pennsylvania (US)
- Univ. of Pennsylvania
- Massachusetts General Hospital (US)
- ORCID
- Venue
- Journal of Medical Imaging
- Publisher
- SPIE-Intl Soc Optical Eng
- Published
- 2018-01-11
- Volume
- 5
- Issue
- 01
- Pages
- 1-21
- ISSN
- 2329-4302 (ISSN-L)
- 2329-4310
- PMC
- PMC5764116
- OpenAlex
- W2783923767
- Cited by
- 204
- References
- 76
- Citations by year
- 2026: 9 · 2025: 30 · 2024: 32 · 2023: 34 · 2022: 26 · 2021: 26 · 2020: 23 · 2019: 18 · 2018: 6
- FWCI
- 12.49
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of medical imaging (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (U24CA189523-01A1)
- US National Institutes of Health (R01NS042645-11A1, R01NS096606, R01CA197000-02)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- radiographic image analysis, brain glioma, breast cancer, lung cancer, treatment response prediction, prognostic factors, predictive biomarkers, texture analysis, connectomics, machine learning, precision diagnostics, risk assessment
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